Most finance sites hide their numbers from AI engines behind un-schema’d JavaScript rate tables. The product is strong, the stack is citation-unfriendly, and the engine guesses your APR from somewhere else. We fix the layer underneath: crawl and render, Core Web Vitals, FinancialProduct schema with accurate rate and fee data, llms.txt and crawler access, and rate tables a parser can read. The work that lets an engine quote your correct number.
Most finance sites hide their numbers from engines behind un-schema’d JavaScript rate tables. The technical layer is what lets an engine quote the right APR. Generic technical SEO gets a site crawled and fast. Finance technical GEO does that and then exposes the one thing the engine came for: the actual rate, as a labeled number it can read in raw HTML and trust from structured data, not a figure painted in by a script the parser never runs.
This is the foundation the rest of a finance program is built on. You can write the best rates page in the category, but if an engine cannot parse the APR off it, the engine pulls a stale number from an aggregator and quotes that in your name. Get the data layer right and the engine reads you. Get it wrong and nothing on top of it lands.
Most engagements run all six in order, because each one depends on the last. You can scope a single layer if that’s where the gap is.
Before any schema goes on, we confirm engines can reach and render your pages. We map what a crawler sees versus what a customer sees, find the rate content stranded behind client-side JavaScript, and hold render speed and stability to the bar a YMYL category warrants. A page an engine cannot load cleanly is a page it cannot quote.
We mark up your products with the right schema.org subtype, LoanOrCredit for loans and cards, BankAccount for accounts, InvestmentOrDeposit for deposits, and populate annualPercentageRate, interestRate, and feesAndCommissionsSpecification with current, accurate values. The engine stops guessing which number is the headline APR because the markup tells it.
BreadcrumbList so engines understand where a rate page sits in your site, and FAQPage so a precise question maps to a precise answer in machine-readable form. To be clear, FAQ rich results in Google are gone as of 2026, so we never sell this as SERP accordions. FAQPage markup here is structured Q&A that AI engines and parsers can lift directly.
We publish llms.txt to signal which content you want AI systems to use, and, more importantly, set robots and agent handling so GPTBot, PerplexityBot, and ClaudeBot can actually reach your rate pages. Access is the part that decides whether any of the parsing work matters. We get it right rather than assume the defaults are.
The headline fix for finance. We move your rates out of client-side widgets and into server-rendered HTML, so the figure a parser reads is the figure the customer sees. The table can still be interactive for humans, but the raw number lives in the markup, where an engine that does not run your JavaScript can still read it correctly.
Structured rates are only as good as how current they are. We build the workflow that keeps the schema, the HTML, and the live rate in sync, so a rate change updates everywhere an engine can read it. This connects to the people who write and watch the numbers: see content strategy for finance and GEO optimization for finance.
A generalist shop can mark up your rates, watch the validator turn green, and call it done, never knowing whether ChatGPT or Perplexity actually quotes the number. We built the instrument that closes that loop. Software that watches what the engines say, plus a senior team that does the implementation, tuned to a regulated category.
Validation tells you the markup is well formed. It does not tell you the engine read it. Geology watches what ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews actually quote about your rates, every week, and flags where the number in the answer does not match the number on your page. That is the difference between a schema audit that looks finished and a data layer the engines can be proven to parse.
The value of accurate rate markup is the same as the risk of inaccurate rate markup. A wrong number an engine quotes in your name raises the same consumer-harm concern the financial-promotions rules exist to prevent, and those rules are technology-neutral. So we treat the freshness and accuracy of structured rate data as a compliance-adjacent question, not a tagging chore, and we route the numbers we publish past your reviewers. One senior team owns the render, the schema, and the access, no junior hand-off.
We crawl the site the way an AI engine does and compare it to what a customer sees. Where the rate content lives behind client-side JavaScript, where a crawler hits a loading state instead of a number, where render speed or stability falls below a YMYL bar. The output is a page-by-page map of every place your real numbers are invisible to a parser.
We add the right FinancialProduct subtype to each product, LoanOrCredit, BankAccount, or InvestmentOrDeposit, with current annualPercentageRate, interestRate, and feesAndCommissionsSpecification values, plus BreadcrumbList and FAQPage markup for structure and parseable Q&A. Every block validates clean, and every number matches the live page before it ships.
We publish llms.txt to signal the content you want AI systems to use, and set robots and agent handling so GPTBot, PerplexityBot, and ClaudeBot can reach your rate pages. Access is the step that decides whether the schema and HTML work pays off, so we confirm the crawlers get through rather than trust the defaults.
We confirm the rate exists in server-rendered HTML, that the structured data carries the matching value, and that an AI crawler reaches it. Then we test the real question: when a customer asks an engine about your rate, does the answer name your correct figure and cite your page. Validation is the floor here, parse accuracy is the proof.
We watch what ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews quote about your rates and flag every gap between the number on your page and the number in the answer. When an engine still quotes a stale or wrong figure, we trace it to a specific page, fix the parse or access cause, and confirm the correction lands.
Run a Live Audit. We check whether your rate pages are parseable, whether AI crawlers can reach them, and whether ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews quote your correct APR or a stale number, and send the full report to your inbox.
